Field
The present invention relates to the field of automated, computerized driver assistance systems for air, land or sea vehicles, where for example a sensor of a host vehicle physically senses the environment of the host vehicle to acquire data describing a traffic environment, and a computing unit which computes, based on acquired data, information which assists the driver in the guidance of the host vehicle. The invention is in particular in the area of predictive emergency automated driver assistance systems (ADAS) that predict future trajectories of other traffic participants in order to enable the host vehicle driver to evade collisions with the other traffic participants or enable the ADAS system of the host vehicle to evade the collisions autonomously.
Description of the Related Art
Driver assistance systems such as “Adaptive Cruise Control” increase driver comfort and safety. They are currently especially used for carrying out longitudinal control of a host vehicle, for example with respect to a velocity specified by the driver and ranging to other traffic objects such as other land, air or sea vehicles (cars, motorbikes, bikes, trucks, etc.), pedestrians, . . . . The host vehicle is provided with (“hosts”) a sensor physically sensing the host vehicle's environment and a computing unit processing an output signal of the at least one sensor. Lateral control of the host vehicle in combination with the longitudinal control is of particular importance for avoiding collisions with other traffic participants. Avoiding collisions is one of the central aims of future ADAS. When emergency situations such as a close-to-crash situation or an exceptional situation such as a crash directly in front of the own vehicle arise, drivers appreciate the assistance of ADAS components such as a collision-mitigation-and-brake system (CMBS). A CMBS system that controls autonomously decelerating the host vehicle to a stand-still or evading other traffic objects by steering commands to avoid or mitigate collisions of the host vehicle with other traffic objects.
When a close-to-crash situation arises, the earlier reactions of the host vehicle to the traffic situation are initiated, the higher is the probability of effectively avoiding a collision. If the host vehicle starts applying a brake or executing an evasion manoeuvre earlier, required actions are less extreme and the probability for avoiding the collision is altogether increased. In order to be able to react earlier, a reliable prediction of the movement of other traffic objects is of high importance. This especially applies in the case of two other traffic objects colliding and suddenly and abruptly changing both their velocity and their direction after the collision occurred.
Under this circumstance a host vehicle is a vehicle in a traffic situation which has the driver assistance system according to the invention mounted thereon and which is equipped with data acquiring means and a computing system that allows the computation of a likely future behavior of other traffic vehicles. The host vehicle is sometimes also referenced as ego-vehicle.
A sensor may be any means that can deliver information suitable for describing at least some aspects of a traffic scene at a point in time. Such sensors may be cameras, radar, lidar that are different types and may form part of or the entire data acquisition means which may comprise even communications means as discussed later with respect to an embodiment.
A target object that may be a target vehicle is an object being observed by generating information by at least one sensor and for which a future behavior shall be estimated. Other objects may be pedestrians, bicyclists or the like.
In prior art there are mentioned systems which evaluate the current state of other traffic participants in a traffic scene to detect critical situations and to trigger an appropriate action in response to the detected critical situation. Appropriate actions are for example warnings or emergency braking in order to avoid or to mitigate a crash with another traffic object.
A further category of assistance systems is configured to apply traffic situation models to acquire data describing a traffic environment (sensor data) in order to generate prediction information describing the future movement of the other traffic objects in usual traffic situations. Based on this prediction information on future movements those systems plan or initiate further actions like warning or calculating an evasion trajectory for the host vehicle.
All systems described in the related work base their recommended actions such as warnings, braking or evasion manoeuvres solely on information available at the host vehicle and conventional knowledge on vehicle agility. Only those manoeuvres of the other traffic objects are taken into consideration which appear suitable to avoid a crash.
U.S. Pat. No. 8,260,538 B2 describes a system detecting an obstacle, determining the current distance and relative velocity to the obstacle and based on a time-to-contact (TTC) until colliding with the obstacle, initiating multiple staged actions comprising warning and different levels of braking. A future movement of the obstacle is not taken into consideration.
WO 2010/000521 describes a system that detects traffic relevant objects in the vicinity of the host vehicle and predicts the future trajectory of the host vehicle as well as of at least one other traffic object. Using the perceived environment it determines the criticality of the traffic object in order to predict an evasion trajectory for the host vehicle. Based on this predicted evasion trajectory the system can initiate at least one of the following actions: showing a permanent path recommendation to the driver, warning the driver, applying a vehicle brake and steering.
None of the cited documents makes a prediction for a movement of the colliding traffic objects after a crash and therefore the state of the art lacks any perception of the dynamics of the crash for movement prediction of traffic objects involved in such collision.
The discussed state of the art requires improved collision avoidance capabilities when compared to the known methods available today.